Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

High throughput, error corrected Nanopore single cell transcriptome sequencing

View ORCID ProfileKevin Lebrigand, View ORCID ProfileVirginie Magnone, View ORCID ProfilePascal Barbry, View ORCID ProfileRainer Waldmann
doi: https://doi.org/10.1101/831495
Kevin Lebrigand
1Université Côte d’Azur, CNRS, Institut de Pharmacologie Moléculaire et Cellulaire, F06560 Sophia Antipolis, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kevin Lebrigand
Virginie Magnone
1Université Côte d’Azur, CNRS, Institut de Pharmacologie Moléculaire et Cellulaire, F06560 Sophia Antipolis, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Virginie Magnone
Pascal Barbry
1Université Côte d’Azur, CNRS, Institut de Pharmacologie Moléculaire et Cellulaire, F06560 Sophia Antipolis, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Pascal Barbry
  • For correspondence: barbry@ipmc.cnrs.fr
Rainer Waldmann
1Université Côte d’Azur, CNRS, Institut de Pharmacologie Moléculaire et Cellulaire, F06560 Sophia Antipolis, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Rainer Waldmann
  • For correspondence: barbry@ipmc.cnrs.fr
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

ABSTRACT

Droplet-based high throughput single cell isolation techniques tremendously boosted the throughput of single cell transcriptome profiling experiments. However, those approaches only allow analysis of one extremity of the transcript after short read sequencing. We introduce an approach that combines Oxford Nanopore sequencing with unique molecular identifiers to obtain error corrected full length sequence information with the 10×Genomics single cell isolation system. This allows to examine differential RNA splicing and RNA editing at a single cell level.

Footnotes

  • https://github.com/ucagenomix/sicelore

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted November 05, 2019.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
High throughput, error corrected Nanopore single cell transcriptome sequencing
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
High throughput, error corrected Nanopore single cell transcriptome sequencing
Kevin Lebrigand, Virginie Magnone, Pascal Barbry, Rainer Waldmann
bioRxiv 831495; doi: https://doi.org/10.1101/831495
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
High throughput, error corrected Nanopore single cell transcriptome sequencing
Kevin Lebrigand, Virginie Magnone, Pascal Barbry, Rainer Waldmann
bioRxiv 831495; doi: https://doi.org/10.1101/831495

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4115)
  • Biochemistry (8818)
  • Bioengineering (6522)
  • Bioinformatics (23466)
  • Biophysics (11792)
  • Cancer Biology (9212)
  • Cell Biology (13326)
  • Clinical Trials (138)
  • Developmental Biology (7439)
  • Ecology (11413)
  • Epidemiology (2066)
  • Evolutionary Biology (15155)
  • Genetics (10439)
  • Genomics (14045)
  • Immunology (9173)
  • Microbiology (22159)
  • Molecular Biology (8814)
  • Neuroscience (47581)
  • Paleontology (350)
  • Pathology (1429)
  • Pharmacology and Toxicology (2492)
  • Physiology (3731)
  • Plant Biology (8082)
  • Scientific Communication and Education (1437)
  • Synthetic Biology (2221)
  • Systems Biology (6039)
  • Zoology (1253)